1 Aggregated and atomic scores per method

#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
#> 
#> Attaching package: 'plotly'
#> The following object is masked from 'package:ggplot2':
#> 
#>     last_plot
#> The following object is masked from 'package:stats':
#> 
#>     filter
#> The following object is masked from 'package:graphics':
#> 
#>     layout


# datasets = read_yaml("datasets.yml") 
# print(score_file)

# datasets = read_yaml("datasets.yml") 
# datasets = read_yaml(file_dataset) 


list_wd = strsplit(getwd(),'/')[[1]]
# Snakemake script : the current working dir is hadaca3_framework
if(list_wd[length(list_wd)] == 'hadaca3_framework'){
  score_files = list(list.files(path = "./output/scores/", full.names = TRUE))

# nextflow script :
}else{
  score_files = list(list.files(pattern = 'score-li*' ))
}


results_li <- data.frame(
  dataset = character(),
  ref = character(),

  preprocessing_mixRNA = character(),
  feature_selection_mixRNA = character(),

  preprocessing_RNA = character(),
  feature_selection_RNA = character(),

  preprocessing_scRNA = character(),
  feature_selection_scRNA = character(),
  deconvolution_rna = character(),

  preprocessing_mixMET = character(),
  feature_selection_mixMET = character(),

  preprocessing_MET = character(),
  feature_selection_MET = character(),
  deconvolution_met = character(),
  late_integration = character(),
  
  aid = numeric(),
  aid_norm = numeric(),
  aitchison = numeric(),
  aitchison_norm = numeric(),
  jsd = numeric(),
  jsd_norm = numeric(),
  mae = numeric(),
  mae_norm = numeric(),
  pearson_col = numeric(),
  pearson_col_norm = numeric(),
  pearson_row = numeric(),
  pearson_row_norm = numeric(),
  pearson_tot = numeric(),
  pearson_tot_norm = numeric(),
  rmse = numeric(),
  rmse_norm = numeric(),
  score_aggreg = numeric(),
  sdid = numeric(),
  sdid_norm = numeric(),
  spearman_col = numeric(),
  spearman_col_norm = numeric(),
  spearman_row = numeric(),
  spearman_row_norm = numeric(),
  spearman_tot = numeric(),
  spearman_tot_norm = numeric()
)


i = 0 
for (score_file in score_files[[1]]) {
  # Extract the base name of the file

  base_name <- basename(score_file)

  # Extract components from the file name

  components <- str_match(base_name, 
  #       dt   ref  OMIC  ppmR fsmR omic ppR fsR omic  ppSR fsSR  deR   omic  ppmM fsmM omic ppM  fsM  deM  li
  # "score-(.+)_(.+)_mixRNA_(.+)_(.+)_RNA_(.+)_(.+)_scRNA_(.+)_(.+)_(.+)_mixMET_(.+)_(.+)_MET_(.+)_(.+)_(.+)_(.+).h5")[2:16]
  "score-li-(.+)_(.+)_mixRNA_(.+)_(.+)_RNA_(.+)_(.+)_scRNA_(.+)_(.+)_(.+)_mixMET_(.+)_(.+)_MET_(.+)_(.+)_(.+)_(.+).h5")[2:16]
  

  # components <- str_match(base_name, "score-(.+)_(.+)_(.+)_(.+)_(.+)_(.+)_(.+)_(.+)")[2:8]
  scores <- read_hdf5(score_file)
  # Append the extracted information to the results data frame
  results_li <- rbind(results_li,
    cbind(
     data.frame(
       dataset = components[1],
       ref = components[2],

       preprocessing_mixRNA = components[3],
       feature_selection_mixRNA = components[4],

       preprocessing_RNA = components[5],
       feature_selection_RNA = components[6],

       preprocessing_scRNA = components[7],
       feature_selection_scRNA = components[8],
       deconvolution_rna = components[9],

        preprocessing_mixMET = components[10],
       feature_selection_mixMET = components[11],

       preprocessing_MET = components[12],
       feature_selection_MET = components[13],
       deconvolution_met = components[14],

       late_integration = components[15],
       stringsAsFactors = FALSE
     ),
     scores
    ))
  rownames(results_li) = NULL

  i = i +1 
}
results_li = results_li[results_li$preprocessing_mixRNA != "nopp",]
results_li = results_li[results_li$feature_selection_mixRNA != "nofs",]
results_li = results_li[results_li$preprocessing_RNA != "nopp",]
results_li = results_li[results_li$feature_selection_RNA != "nofs",]
results_li = results_li[results_li$preprocessing_scRNA != "nopp",]
results_li = results_li[results_li$feature_selection_scRNA != "nofs",]
results_li = results_li[results_li$deconvolution_rna != "node",]
results_li = results_li[results_li$preprocessing_mixMET != "nopp",]
results_li = results_li[results_li$feature_selection_mixMET != "nofs",]
results_li = results_li[results_li$preprocessing_MET != "nopp",]
results_li = results_li[results_li$feature_selection_MET != "nofs",]
results_li = results_li[results_li$deconvolution_met != "node",]

results_li %>%
  # filter(dc==2) %>%
  group_by(late_integration) %>%
  summarise(GlobalScore = median(score_aggreg)) %>%
  arrange(desc(GlobalScore))
#> # A tibble: 1 × 2
#>   late_integration GlobalScore
#>   <chr>                  <dbl>
#> 1 limeanRMSE             0.660


results_li_top5 = results_li %>%
  select(dataset:late_integration, score_aggreg) %>% 
  arrange(desc(score_aggreg)) %>% 
  slice_head(n = 5)
results_li_top5
#>                    dataset ref preprocessing_mixRNA feature_selection_mixRNA
#> 1 insilicodirichletCopule1 ref              LogNorm            Toastbulknbfs
#> 2 insilicodirichletCopule1 ref              LogNorm            Toastbulknbfs
#> 3 insilicodirichletCopule1 ref              LogNorm            Toastbulknbfs
#> 4 insilicodirichletCopule1 ref              LogNorm            Toastbulknbfs
#> 5 insilicodirichletCopule1 ref                 ppID            Toastbulknbfs
#>   preprocessing_RNA feature_selection_RNA preprocessing_scRNA
#> 1           LogNorm         Toastbulknbfs             LogNorm
#> 2           LogNorm         Toastbulknbfs             LogNorm
#> 3           LogNorm         Toastbulknbfs                ppID
#> 4           LogNorm         Toastbulknbfs                ppID
#> 5              ppID         Toastbulknbfs             LogNorm
#>   feature_selection_scRNA deconvolution_rna preprocessing_mixMET
#> 1                    fsID        RLRpoisson            normalize
#> 2           Toastbulknbfs        RLRpoisson            normalize
#> 3                    fsID        RLRpoisson            normalize
#> 4           Toastbulknbfs        RLRpoisson            normalize
#> 5                    fsID        RLRpoisson            normalize
#>   feature_selection_mixMET preprocessing_MET feature_selection_MET
#> 1           mostmethylated         normalize        mostmethylated
#> 2           mostmethylated         normalize        mostmethylated
#> 3           mostmethylated         normalize        mostmethylated
#> 4           mostmethylated         normalize        mostmethylated
#> 5           mostmethylated         normalize        mostmethylated
#>   deconvolution_met late_integration score_aggreg
#> 1        RLRpoisson       limeanRMSE    0.7833919
#> 2        RLRpoisson       limeanRMSE    0.7833919
#> 3        RLRpoisson       limeanRMSE    0.7833919
#> 4        RLRpoisson       limeanRMSE    0.7833919
#> 5        RLRpoisson       limeanRMSE    0.7829674

prediction_file = sapply(1:nrow(results_li_top5), \(i){paste0("pred-li-",paste(results_li_top5[i,1:15],collapse = "_") ,".h5")})
pred = lapply(prediction_file, \(path){read_hdf5(path)})

pred
#> [[1]]
#> [[1]]$pred
#>              [,1]       [,2]       [,3]       [,4]      [,5]      [,6]
#> endo    0.1521071 0.18589690 0.18731846 0.16722981 0.2425523 0.1196690
#> fibro   0.3640816 0.37276015 0.44188177 0.51775550 0.4273091 0.5182906
#> immune  0.1151060 0.01246485 0.03708931 0.03192019 0.0487975 0.0000000
#> classic 0.1709578 0.21615714 0.16256007 0.14330491 0.1430618 0.1761545
#> basal   0.1977475 0.21272097 0.17115039 0.13978959 0.1382793 0.1858859
#>              [,7]      [,8]       [,9]      [,10]      [,11]      [,12]
#> endo    0.1779348 0.1361258 0.11851552 0.17461486 0.14241835 0.17775027
#> fibro   0.3856826 0.4662586 0.29611475 0.53547942 0.37782893 0.33925032
#> immune  0.2011419 0.0216533 0.04088747 0.02629235 0.01569903 0.04642653
#> classic 0.1150575 0.1802985 0.26133309 0.12689810 0.22570489 0.21778814
#> basal   0.1201833 0.1956638 0.28314916 0.13671527 0.23834881 0.21878475
#>              [,13]     [,14]     [,15]     [,16]      [,17]     [,18]
#> endo    0.11505408 0.1490773 0.2230690 0.1352964 0.22097151 0.1786065
#> fibro   0.33983213 0.3719341 0.2379239 0.2745355 0.35362157 0.3042754
#> immune  0.02473837 0.0315475 0.2200350 0.1219688 0.07830546 0.0654633
#> classic 0.24958195 0.2184365 0.1639787 0.2301259 0.16518393 0.2152541
#> basal   0.27079346 0.2290046 0.1549935 0.2380734 0.18191752 0.2364007
#>               [,19]     [,20]     [,21]      [,22]     [,23]      [,24]
#> endo    0.188096385 0.1834232 0.1815480 0.13097155 0.1395175 0.09237389
#> fibro   0.474001230 0.4167431 0.3536357 0.48197661 0.4214840 0.37815392
#> immune  0.002126289 0.1539702 0.1200235 0.09911739 0.0118145 0.12069201
#> classic 0.154967867 0.1222691 0.1685829 0.14071420 0.2007344 0.18853796
#> basal   0.180808229 0.1235943 0.1762099 0.14722026 0.2264496 0.22024222
#>              [,25]     [,26]      [,27]       [,28]     [,29]     [,30]
#> endo    0.21666351 0.1896891 0.15463961 0.174534508 0.1436742 0.1807047
#> fibro   0.43920609 0.3598660 0.43725625 0.326870145 0.4468374 0.3574416
#> immune  0.05857245 0.1486802 0.02255197 0.002461908 0.0402629 0.1473147
#> classic 0.14093980 0.1552845 0.17966206 0.236799769 0.1776290 0.1537884
#> basal   0.14461815 0.1464801 0.20589011 0.259333669 0.1915966 0.1607506
#> 
#> 
#> [[2]]
#> [[2]]$pred
#>              [,1]       [,2]       [,3]       [,4]      [,5]      [,6]
#> endo    0.1521071 0.18589690 0.18731846 0.16722981 0.2425523 0.1196690
#> fibro   0.3640816 0.37276015 0.44188177 0.51775550 0.4273091 0.5182906
#> immune  0.1151060 0.01246485 0.03708931 0.03192019 0.0487975 0.0000000
#> classic 0.1709578 0.21615714 0.16256007 0.14330491 0.1430618 0.1761545
#> basal   0.1977475 0.21272097 0.17115039 0.13978959 0.1382793 0.1858859
#>              [,7]      [,8]       [,9]      [,10]      [,11]      [,12]
#> endo    0.1779348 0.1361258 0.11851552 0.17461486 0.14241835 0.17775027
#> fibro   0.3856826 0.4662586 0.29611475 0.53547942 0.37782893 0.33925032
#> immune  0.2011419 0.0216533 0.04088747 0.02629235 0.01569903 0.04642653
#> classic 0.1150575 0.1802985 0.26133309 0.12689810 0.22570489 0.21778814
#> basal   0.1201833 0.1956638 0.28314916 0.13671527 0.23834881 0.21878475
#>              [,13]     [,14]     [,15]     [,16]      [,17]     [,18]
#> endo    0.11505408 0.1490773 0.2230690 0.1352964 0.22097151 0.1786065
#> fibro   0.33983213 0.3719341 0.2379239 0.2745355 0.35362157 0.3042754
#> immune  0.02473837 0.0315475 0.2200350 0.1219688 0.07830546 0.0654633
#> classic 0.24958195 0.2184365 0.1639787 0.2301259 0.16518393 0.2152541
#> basal   0.27079346 0.2290046 0.1549935 0.2380734 0.18191752 0.2364007
#>               [,19]     [,20]     [,21]      [,22]     [,23]      [,24]
#> endo    0.188096385 0.1834232 0.1815480 0.13097155 0.1395175 0.09237389
#> fibro   0.474001230 0.4167431 0.3536357 0.48197661 0.4214840 0.37815392
#> immune  0.002126289 0.1539702 0.1200235 0.09911739 0.0118145 0.12069201
#> classic 0.154967867 0.1222691 0.1685829 0.14071420 0.2007344 0.18853796
#> basal   0.180808229 0.1235943 0.1762099 0.14722026 0.2264496 0.22024222
#>              [,25]     [,26]      [,27]       [,28]     [,29]     [,30]
#> endo    0.21666351 0.1896891 0.15463961 0.174534508 0.1436742 0.1807047
#> fibro   0.43920609 0.3598660 0.43725625 0.326870145 0.4468374 0.3574416
#> immune  0.05857245 0.1486802 0.02255197 0.002461908 0.0402629 0.1473147
#> classic 0.14093980 0.1552845 0.17966206 0.236799769 0.1776290 0.1537884
#> basal   0.14461815 0.1464801 0.20589011 0.259333669 0.1915966 0.1607506
#> 
#> 
#> [[3]]
#> [[3]]$pred
#>              [,1]       [,2]       [,3]       [,4]      [,5]      [,6]
#> endo    0.1521071 0.18589690 0.18731846 0.16722981 0.2425523 0.1196690
#> fibro   0.3640816 0.37276015 0.44188177 0.51775550 0.4273091 0.5182906
#> immune  0.1151060 0.01246485 0.03708931 0.03192019 0.0487975 0.0000000
#> classic 0.1709578 0.21615714 0.16256007 0.14330491 0.1430618 0.1761545
#> basal   0.1977475 0.21272097 0.17115039 0.13978959 0.1382793 0.1858859
#>              [,7]      [,8]       [,9]      [,10]      [,11]      [,12]
#> endo    0.1779348 0.1361258 0.11851552 0.17461486 0.14241835 0.17775027
#> fibro   0.3856826 0.4662586 0.29611475 0.53547942 0.37782893 0.33925032
#> immune  0.2011419 0.0216533 0.04088747 0.02629235 0.01569903 0.04642653
#> classic 0.1150575 0.1802985 0.26133309 0.12689810 0.22570489 0.21778814
#> basal   0.1201833 0.1956638 0.28314916 0.13671527 0.23834881 0.21878475
#>              [,13]     [,14]     [,15]     [,16]      [,17]     [,18]
#> endo    0.11505408 0.1490773 0.2230690 0.1352964 0.22097151 0.1786065
#> fibro   0.33983213 0.3719341 0.2379239 0.2745355 0.35362157 0.3042754
#> immune  0.02473837 0.0315475 0.2200350 0.1219688 0.07830546 0.0654633
#> classic 0.24958195 0.2184365 0.1639787 0.2301259 0.16518393 0.2152541
#> basal   0.27079346 0.2290046 0.1549935 0.2380734 0.18191752 0.2364007
#>               [,19]     [,20]     [,21]      [,22]     [,23]      [,24]
#> endo    0.188096385 0.1834232 0.1815480 0.13097155 0.1395175 0.09237389
#> fibro   0.474001230 0.4167431 0.3536357 0.48197661 0.4214840 0.37815392
#> immune  0.002126289 0.1539702 0.1200235 0.09911739 0.0118145 0.12069201
#> classic 0.154967867 0.1222691 0.1685829 0.14071420 0.2007344 0.18853796
#> basal   0.180808229 0.1235943 0.1762099 0.14722026 0.2264496 0.22024222
#>              [,25]     [,26]      [,27]       [,28]     [,29]     [,30]
#> endo    0.21666351 0.1896891 0.15463961 0.174534508 0.1436742 0.1807047
#> fibro   0.43920609 0.3598660 0.43725625 0.326870145 0.4468374 0.3574416
#> immune  0.05857245 0.1486802 0.02255197 0.002461908 0.0402629 0.1473147
#> classic 0.14093980 0.1552845 0.17966206 0.236799769 0.1776290 0.1537884
#> basal   0.14461815 0.1464801 0.20589011 0.259333669 0.1915966 0.1607506
#> 
#> 
#> [[4]]
#> [[4]]$pred
#>              [,1]       [,2]       [,3]       [,4]      [,5]      [,6]
#> endo    0.1521071 0.18589690 0.18731846 0.16722981 0.2425523 0.1196690
#> fibro   0.3640816 0.37276015 0.44188177 0.51775550 0.4273091 0.5182906
#> immune  0.1151060 0.01246485 0.03708931 0.03192019 0.0487975 0.0000000
#> classic 0.1709578 0.21615714 0.16256007 0.14330491 0.1430618 0.1761545
#> basal   0.1977475 0.21272097 0.17115039 0.13978959 0.1382793 0.1858859
#>              [,7]      [,8]       [,9]      [,10]      [,11]      [,12]
#> endo    0.1779348 0.1361258 0.11851552 0.17461486 0.14241835 0.17775027
#> fibro   0.3856826 0.4662586 0.29611475 0.53547942 0.37782893 0.33925032
#> immune  0.2011419 0.0216533 0.04088747 0.02629235 0.01569903 0.04642653
#> classic 0.1150575 0.1802985 0.26133309 0.12689810 0.22570489 0.21778814
#> basal   0.1201833 0.1956638 0.28314916 0.13671527 0.23834881 0.21878475
#>              [,13]     [,14]     [,15]     [,16]      [,17]     [,18]
#> endo    0.11505408 0.1490773 0.2230690 0.1352964 0.22097151 0.1786065
#> fibro   0.33983213 0.3719341 0.2379239 0.2745355 0.35362157 0.3042754
#> immune  0.02473837 0.0315475 0.2200350 0.1219688 0.07830546 0.0654633
#> classic 0.24958195 0.2184365 0.1639787 0.2301259 0.16518393 0.2152541
#> basal   0.27079346 0.2290046 0.1549935 0.2380734 0.18191752 0.2364007
#>               [,19]     [,20]     [,21]      [,22]     [,23]      [,24]
#> endo    0.188096385 0.1834232 0.1815480 0.13097155 0.1395175 0.09237389
#> fibro   0.474001230 0.4167431 0.3536357 0.48197661 0.4214840 0.37815392
#> immune  0.002126289 0.1539702 0.1200235 0.09911739 0.0118145 0.12069201
#> classic 0.154967867 0.1222691 0.1685829 0.14071420 0.2007344 0.18853796
#> basal   0.180808229 0.1235943 0.1762099 0.14722026 0.2264496 0.22024222
#>              [,25]     [,26]      [,27]       [,28]     [,29]     [,30]
#> endo    0.21666351 0.1896891 0.15463961 0.174534508 0.1436742 0.1807047
#> fibro   0.43920609 0.3598660 0.43725625 0.326870145 0.4468374 0.3574416
#> immune  0.05857245 0.1486802 0.02255197 0.002461908 0.0402629 0.1473147
#> classic 0.14093980 0.1552845 0.17966206 0.236799769 0.1776290 0.1537884
#> basal   0.14461815 0.1464801 0.20589011 0.259333669 0.1915966 0.1607506
#> 
#> 
#> [[5]]
#> [[5]]$pred
#>              [,1]       [,2]       [,3]       [,4]      [,5]      [,6]
#> endo    0.1516212 0.18557596 0.18728482 0.16714354 0.2423916 0.1198319
#> fibro   0.3602391 0.36979272 0.44009159 0.51687221 0.4261273 0.5161957
#> immune  0.1184958 0.01480753 0.03770895 0.03198968 0.0490858 0.0000000
#> classic 0.1712810 0.21620232 0.16256378 0.14323721 0.1430556 0.1764022
#> basal   0.1983629 0.21362147 0.17235087 0.14075736 0.1393397 0.1875702
#>              [,7]       [,8]       [,9]      [,10]      [,11]      [,12]
#> endo    0.1772867 0.13612665 0.11771890 0.17435430 0.14237645 0.17762951
#> fibro   0.3822496 0.46380994 0.29453980 0.53200159 0.37584410 0.33700328
#> immune  0.2037650 0.02260563 0.04343975 0.02773796 0.01717599 0.04729253
#> classic 0.1155581 0.18036773 0.26164321 0.12743644 0.22543944 0.21781232
#> basal   0.1211406 0.19709005 0.28265834 0.13846971 0.23916402 0.22026235
#>              [,13]      [,14]     [,15]     [,16]      [,17]      [,18]
#> endo    0.11517104 0.14895442 0.2219482 0.1348663 0.22012318 0.17811381
#> fibro   0.33751651 0.36941897 0.2354276 0.2717808 0.35101142 0.30139336
#> immune  0.02666597 0.03283758 0.2229607 0.1244608 0.08148366 0.06828831
#> classic 0.24929900 0.21853958 0.1640731 0.2304053 0.16512537 0.21526446
#> basal   0.27134747 0.23024944 0.1555904 0.2384868 0.18225637 0.23694006
#>              [,19]     [,20]     [,21]     [,22]      [,23]      [,24]
#> endo    0.18796456 0.1826527 0.1805234 0.1310479 0.13952973 0.09225906
#> fibro   0.47164846 0.4128160 0.3507678 0.4783769 0.41869165 0.37438850
#> immune  0.00329991 0.1569174 0.1227414 0.1014937 0.01298726 0.12359803
#> classic 0.15513086 0.1228646 0.1689692 0.1408919 0.20090673 0.18890329
#> basal   0.18195620 0.1247493 0.1769981 0.1481896 0.22788463 0.22085111
#>              [,25]     [,26]     [,27]      [,28]      [,29]     [,30]
#> endo    0.21603226 0.1892314 0.1546453 0.17423442 0.14302748 0.1806570
#> fibro   0.43664061 0.3572180 0.4342529 0.32451536 0.44308068 0.3546594
#> immune  0.06020253 0.1510027 0.0247478 0.00361847 0.04352868 0.1496000
#> classic 0.14131147 0.1552811 0.1796548 0.23693327 0.17798508 0.1537389
#> basal   0.14581313 0.1472669 0.2066992 0.26069847 0.19237808 0.1613447


all_data_used = c('dataset', 'ref')
for(data_used in all_data_used){
  results_li[[data_used]] = factor(results_li[[data_used]], 
  levels = unique(results_li[[data_used]])) # levels will be alphabeticaly ordered
}



all_functions_li = c('preprocessing_mixRNA', 'feature_selection_mixRNA', 'preprocessing_RNA', 'feature_selection_RNA', 'preprocessing_scRNA', 'feature_selection_scRNA', 'deconvolution_rna', 'preprocessing_mixMET', 'feature_selection_mixMET', 'preprocessing_MET', 'feature_selection_MET', 'deconvolution_met', 'late_integration' )
for( fun in all_functions_li){
  results_li[[fun]] = factor(results_li[[fun]], 
  levels = unique(results_li[[fun]][order(results_li$score_aggreg[results_li$dataset=='invitro1'],decreasing = T)])) # sort based on the results_li on the in vitro dataset
}



index_aggreg <- which(names(results_li) == "score_aggreg")

datatable(
  results_li[, c(1:length(all_functions_li)+2, index_aggreg)],
  extensions = 'Buttons',
  options = list(
    pageLength = 10,
    dom = 'Bfrtip',  # This includes the Buttons extension in the layout
    buttons = list(
      list(
        extend = 'colvis',
        text = 'Show/Hide Columns',
        columns = ':not(:first-child)'  # This allows all columns except the first to be toggled
      )
    )
  )
)

2 Early integration_table

#> # A tibble: 0 × 2
#> # ℹ 2 variables: early_integration <chr>, GlobalScore <dbl>

3 Visualisations of the top 5 methods

3.1 Best combination

4 Visualisations of the different metrics

4.1 Aggregated scores

4.1.1 PP

4.1.2 FS

4.1.3 DE

4.1.4 LI

4.2 MAE

4.2.1 PP

4.2.2 FS

#> Warning: Removed 576 rows containing non-finite outside the scale range
#> (`stat_ydensity()`).
#> Warning: Removed 576 rows containing non-finite outside the scale range
#> (`stat_ydensity()`).

4.2.3 DE

#> Warning: Removed 576 rows containing non-finite outside the scale range
#> (`stat_ydensity()`).
#> Warning: Removed 576 rows containing non-finite outside the scale range
#> (`stat_ydensity()`).

4.2.4 LI

#> Warning: Removed 576 rows containing non-finite outside the scale range
#> (`stat_ydensity()`).

4.3 RMSE

4.3.1 PP

4.3.2 FS

#> Warning: Removed 576 rows containing non-finite outside the scale range
#> (`stat_ydensity()`).
#> Warning: Removed 576 rows containing non-finite outside the scale range
#> (`stat_ydensity()`).

4.3.3 DE

#> Warning: Removed 576 rows containing non-finite outside the scale range
#> (`stat_ydensity()`).
#> Warning: Removed 576 rows containing non-finite outside the scale range
#> (`stat_ydensity()`).

4.3.4 LI

#> Warning: Removed 576 rows containing non-finite outside the scale range
#> (`stat_ydensity()`).

4.4 Spearman correlation (row)

4.4.1 PP

4.4.2 FS

4.4.3 DE

4.4.4 LI

4.5 Aitchison distance

4.5.1 PP

4.5.2 FS

#> Warning: Removed 576 rows containing non-finite outside the scale range
#> (`stat_ydensity()`).
#> Warning: Removed 576 rows containing non-finite outside the scale range
#> (`stat_ydensity()`).

4.5.3 DE

#> Warning: Removed 576 rows containing non-finite outside the scale range
#> (`stat_ydensity()`).
#> Warning: Removed 576 rows containing non-finite outside the scale range
#> (`stat_ydensity()`).

4.5.4 LI

#> Warning: Removed 576 rows containing non-finite outside the scale range
#> (`stat_ydensity()`).